double lemur::cluster::AgglomCluster::score(const ClusterRep *rep) const {
switch (docmode) {
case ClusterParam::DMAX:
return score_max(rep);
case ClusterParam::DMIN:
return score_min(rep);
case ClusterParam::DAVE:
return score_ave(rep);
case ClusterParam::DMEAN:
return score_mean(rep);
default:
return 0;
}
}
void StatisticsBasedScorer::score(const candidates_t& candidates, const diffs_t& diffs,
statscores_t& scores) const
{
if (!m_score_data) {
throw runtime_error("Score data not loaded");
}
// calculate the score for the candidates
if (m_score_data->size() == 0) {
throw runtime_error("Score data is empty");
}
if (candidates.size() == 0) {
throw runtime_error("No candidates supplied");
}
int numCounts = m_score_data->get(0,candidates[0]).size();
vector<int> totals(numCounts);
for (size_t i = 0; i < candidates.size(); ++i) {
ScoreStats stats = m_score_data->get(i,candidates[i]);
if (stats.size() != totals.size()) {
stringstream msg;
msg << "Statistics for (" << "," << candidates[i] << ") have incorrect "
<< "number of fields. Found: " << stats.size() << " Expected: "
<< totals.size();
throw runtime_error(msg.str());
}
for (size_t k = 0; k < totals.size(); ++k) {
totals[k] += stats.get(k);
}
}
scores.push_back(calculateScore(totals));
candidates_t last_candidates(candidates);
// apply each of the diffs, and get new scores
for (size_t i = 0; i < diffs.size(); ++i) {
for (size_t j = 0; j < diffs[i].size(); ++j) {
size_t sid = diffs[i][j].first;
size_t nid = diffs[i][j].second;
size_t last_nid = last_candidates[sid];
for (size_t k = 0; k < totals.size(); ++k) {
int diff = m_score_data->get(sid,nid).get(k)
- m_score_data->get(sid,last_nid).get(k);
totals[k] += diff;
}
last_candidates[sid] = nid;
}
scores.push_back(calculateScore(totals));
}
// Regularisation. This can either be none, or the min or average as described in
// Cer, Jurafsky and Manning at WMT08.
if (m_regularization_type == NONE || m_regularization_window <= 0) {
// no regularisation
return;
}
// window size specifies the +/- in each direction
statscores_t raw_scores(scores); // copy scores
for (size_t i = 0; i < scores.size(); ++i) {
size_t start = 0;
if (i >= m_regularization_window) {
start = i - m_regularization_window;
}
const size_t end = min(scores.size(), i + m_regularization_window + 1);
if (m_regularization_type == AVERAGE) {
scores[i] = score_average(raw_scores,start,end);
} else {
scores[i] = score_min(raw_scores,start,end);
}
}
}
